{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Statistical Tests in Python**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Concepts**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Concept 1 - What is a Statistical Test?**\n",
    "Statistical tests are used in Hypothesis Testing. You can use them to determine:\n",
    "- the relationships between input and output variables\n",
    "- estimate the differences between two or more groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Concept 2 - What is Hypothesis Testing?**\n",
    "Hypothesis testing is a way for scientists and researchers to make decisions about whether their ideas, or hypotheses, are supported by evidence from their experiments or observations.\n",
    "\n",
    "Here's a simple breakdown:\n",
    "1. **Formulate a Hypothesis:** Start with an idea or a guess about something you want to study. This is called a hypothesis.\n",
    "2. **Collect Data:** Conduct experiments or gather information to see if the data supports your hypothesis.\n",
    "3. **Test Your Hypothesis:** Use statistical techniques to analyze the data and determine if it provides strong evidence for or against your hypothesis.\n",
    "4. **Decide a Significance Level:** Decide a significance level.\n",
    "5. **Make a Conclusion:** Based on the analysis, you decide whether the data supports your original idea or if it suggests something different.\n",
    "\n",
    "In even simpler terms, hypothesis testing helps us figure out if our educated guesses about how things work are likely to be true or not, based on the evidence we collect. It's like being a detective and using clues to decide if a suspect is guilty or not guilty!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Concept 3 - What is a Type I and Type II Error?**\n",
    "\n",
    "Type I and Type II errors are concepts related to hypothesis testing in statistics. Here's an explanation of each type with examples:\n",
    "\n",
    "#### Type I Error (False Positive):\n",
    "- **Definition:** Type I error occurs when we reject a true null hypothesis. In other words, we incorrectly conclude that there is an effect or difference when there isn't one.\n",
    "- **Symbol:** Often denoted by $( \\alpha )$, the significance level.\n",
    "- **Example:**\n",
    "  - **Scenario:** Testing a new drug's effectiveness.\n",
    "  - **Null Hypothesis $(H_0)$:** The drug has no effect.\n",
    "  - **Type I Error $( \\alpha )$:** Concluding the drug is effective when it actually has no effect. or Concluding the person has commited a crime when he is actually innocent.\n",
    "\n",
    "#### Type II Error (False Negative):\n",
    "- **Definition:** Type II error occurs when we fail to reject a false null hypothesis. In this case, there is a true effect or difference, but our test fails to detect it.\n",
    "- **Symbol:** Often denoted by $( \\beta )$.\n",
    "- **Example:**\n",
    "  - **Scenario:** Testing a new drug's effectiveness.\n",
    "  - **Alternative Hypothesis $(H_1)$:** The drug has a significant effect.\n",
    "  - **Type II Error $( \\beta )$:** Failing to detect the drug's effectiveness when it actually has an effect. or Failing to detect the person has commited a crime when he is actually guilty.\n",
    "\n",
    "#### Relationship:\n",
    "\n",
    "- **Inverse Relationship:** There is typically an inverse relationship between Type I and Type II errors. As you decrease the likelihood of one type of error, the likelihood of the other type of error increases.\n",
    "- **Adjusting Significance Level $( \\alpha )$:** By decreasing the significance level $( \\alpha )$, the probability of Type I error decreases, but the probability of Type II error may increase.\n",
    "\n",
    "It's crucial to strike a balance between these two types of errors based on the specific context and consequences of each error. The choice of significance level and statistical power (the ability to detect true effects) influences the likelihood of Type I and Type II errors in hypothesis testing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Concept 4 - What is p-value?**\n",
    "The p-value, short for \"probability value,\" is a number that helps us understand the strength of evidence against a null hypothesis in hypothesis testing.\n",
    "\n",
    "**OR**\n",
    "\n",
    "p-value quantifies the strength of evidence i.e. how likely the evidence occured by a random chance. The smaller the p-value, less likely the result occured by random change and the stronger the evidence that you should reject the null hypothesis.\n",
    "\n",
    "To say whether the p-value is significant or not, we need a significance threshold called the **significance level**. This threshold is usually set at 0.05. \n",
    "\n",
    "It's important because it helps control the rate of Type I errors. By setting a threshold, we define the level of evidence needed to reject the null hypothesis. Lower thresholds (e.g., 0.01) require stronger evidence.\n",
    "\n",
    "\n",
    "If the p-value is **BELOW** the threshold (meaning smaller than), then you can infer a **statistically significant evidence** i.e. outcome didn't occur randomly.    \n",
    "i.e. For p-value < 0.05, you can Reject the Null Hypothesis\n",
    "\n",
    "Otherwise, then you can infer **no statistically significant evidence** i.e. outcome occured at random.  \n",
    "i.e. For p-value > 0.05, you Fail to Reject the Null Hypothesis\n",
    "\n",
    "Here's a simple explanation:\n",
    "1. **Formulate the Hypothesis**\n",
    "    - `Null Hypothesis (H0):` This is a statement that there is no significant difference or relationship. It's like assuming innocence in a court trial.\n",
    "    - `Alternative Hypothesis (H1):` This is the opposite of the null hypothesis. It suggests that there is a significant difference or relationship.\n",
    "2. **Collect Data:** Perform experiments or gather information.\n",
    "3. **Apply an appropriate Statistical Test**\n",
    "    - `Calculate Test Statistic:` This is a numerical value calculated from your data, depending on the type of test you're conducting (e.g., t-test, F-test, etc.).\n",
    "    - `Calculate p-value:` It's the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from your data, assuming the null hypothesis is true.\n",
    "4. **Decide a Significance Level**\n",
    "5. **Make a Conclusion**\n",
    "    - `if p_value < significance_level` => Reject the null hypothesis $(H_0)$\n",
    "    - `otherwise` => Fail to reject the null hypothesis $(H_0)$\n",
    "\n",
    "Now, let's go through an example:   \n",
    "**Example: Coin Toss**  \n",
    "Suppose you have a coin and you want to test if it's fair (i.e., has an equal chance of landing heads or tails). Here's how hypothesis testing works:  \n",
    "1. Formulate the Hypothesis.  \n",
    "    - `Null Hypothesis (H0):` The coin is fair.\n",
    "    - `Alternative Hypothesis (H1):` The coin is not fair (meaning it's biased).\n",
    "2. Collect a sample.  \n",
    "    - You flip the coin 100 times and get 60 heads and 40 tails.\n",
    "3. You calculate a test statistic   \n",
    "    - In this case, you might use a chi-squared test.\n",
    "    - Let's say the p-value you calculate is 0.03.  \n",
    "4. Decide a significance_level (i.e. alpha = 0.05)  \n",
    "5. Make a conclusion - If you set a significance level (often denoted as alpha, α) at 0.05 (5%), then your p-value of 0.03 is less than α. This means:\n",
    "    - If the coin were fair (null hypothesis is true), you'd expect to see results as extreme as this or more extreme only 3% of the time.\n",
    "    - Since 3% is less than 5%, you have enough evidence to reject the null hypothesis. You conclude that the coin is likely biased.  \n",
    "\n",
    "Remember, a small p-value indicates strong evidence against the null hypothesis, while a large p-value suggests weak evidence against it. However, a p-value alone does not prove or disprove anything; it's just part of the evidence you consider in making a decision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### **Concept 5 - Important Hypothesis Tests**\n",
    "\n",
    "There are many hypothesis tests. Here is a list of few most important tests:\n",
    "#### **Univariate**\n",
    "- **Chi-Square Goodness-of-Fit Test:** Tests whether the observed frequencies of categorical data match the expected frequencies according to a specified distribution.\n",
    "- **Shapiro-Wilk Test:** The Shapiro-Wilk test is a statistical test that checks whether a dataset follows a normal distribution.\n",
    "- **Kolmogorov-Smirnov Test:** Tests whether a sample comes from a specific distribution (not limited to normal distribution). It can be used to compare any two continuous distributions.\n",
    "- **One-Sample t-test:** Tests whether the mean of a single sample is significantly different from a known or hypothesized population mean.\n",
    "\n",
    "#### **Bivariate**\n",
    "- **Pearson Test:** Tests whether two samples have a linear relationship.\n",
    "- **Two-Sample t-test:** Compares the means of two related samples (e.g., before and after treatment) to determine if there is a significant difference.\n",
    "- **ANOVA (one_way_anova):** Compares the mean of three or more samples. It helps determine if there is a significant difference between the samples.\n",
    "- **Chi-Square Test of Independence:** Tests whether there is a significant association between two categorical variables in a contingency table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Importing all the Required Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Loading the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n",
       "0   25    Private  226802          11th                7       Never-married   \n",
       "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
       "4   18          ?  103497  Some-college               10       Never-married   \n",
       "\n",
       "          occupation relationship   race  gender  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
       "1    Farming-fishing      Husband  White    Male             0             0   \n",
       "2    Protective-serv      Husband  White    Male             0             0   \n",
       "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
       "4                  ?    Own-child  White  Female             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  \n",
       "2              40  United-States   >50K  \n",
       "3              40  United-States   >50K  \n",
       "4              30  United-States  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Renaming the Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.strip().str.replace('-', '_')\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   age              48842 non-null  int64 \n",
      " 1   workclass        48842 non-null  object\n",
      " 2   fnlwgt           48842 non-null  int64 \n",
      " 3   education        48842 non-null  object\n",
      " 4   educational_num  48842 non-null  int64 \n",
      " 5   marital_status   48842 non-null  object\n",
      " 6   occupation       48842 non-null  object\n",
      " 7   relationship     48842 non-null  object\n",
      " 8   race             48842 non-null  object\n",
      " 9   gender           48842 non-null  object\n",
      " 10  capital_gain     48842 non-null  int64 \n",
      " 11  capital_loss     48842 non-null  int64 \n",
      " 12  hours_per_week   48842 non-null  int64 \n",
      " 13  native_country   48842 non-null  object\n",
      " 14  income           48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Univariate Analysis - Discrete Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "discrete_df = df.select_dtypes(include=['object'])\n",
    "\n",
    "numerical_df = df.select_dtypes(include=['int64', 'float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discrete_univariate_analysis(discrete_data):\n",
    "    for col_name in discrete_data:\n",
    "        print(\"*\"*10, col_name, \"*\"*10)\n",
    "        print(discrete_data[col_name].agg(['count', 'nunique', 'unique']))\n",
    "        print('Value Counts: \\n', discrete_data[col_name].value_counts())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** workclass **********\n",
      "count                                                  48842\n",
      "nunique                                                    9\n",
      "unique     [Private, Local-gov, ?, Self-emp-not-inc, Fede...\n",
      "Name: workclass, dtype: object\n",
      "Value Counts: \n",
      " workclass\n",
      "Private             33906\n",
      "Self-emp-not-inc     3862\n",
      "Local-gov            3136\n",
      "?                    2799\n",
      "State-gov            1981\n",
      "Self-emp-inc         1695\n",
      "Federal-gov          1432\n",
      "Without-pay            21\n",
      "Never-worked           10\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** education **********\n",
      "count                                                  48842\n",
      "nunique                                                   16\n",
      "unique     [11th, HS-grad, Assoc-acdm, Some-college, 10th...\n",
      "Name: education, dtype: object\n",
      "Value Counts: \n",
      " education\n",
      "HS-grad         15784\n",
      "Some-college    10878\n",
      "Bachelors        8025\n",
      "Masters          2657\n",
      "Assoc-voc        2061\n",
      "11th             1812\n",
      "Assoc-acdm       1601\n",
      "10th             1389\n",
      "7th-8th           955\n",
      "Prof-school       834\n",
      "9th               756\n",
      "12th              657\n",
      "Doctorate         594\n",
      "5th-6th           509\n",
      "1st-4th           247\n",
      "Preschool          83\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** marital_status **********\n",
      "count                                                  48842\n",
      "nunique                                                    7\n",
      "unique     [Never-married, Married-civ-spouse, Widowed, D...\n",
      "Name: marital_status, dtype: object\n",
      "Value Counts: \n",
      " marital_status\n",
      "Married-civ-spouse       22379\n",
      "Never-married            16117\n",
      "Divorced                  6633\n",
      "Separated                 1530\n",
      "Widowed                   1518\n",
      "Married-spouse-absent      628\n",
      "Married-AF-spouse           37\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** occupation **********\n",
      "count                                                  48842\n",
      "nunique                                                   15\n",
      "unique     [Machine-op-inspct, Farming-fishing, Protectiv...\n",
      "Name: occupation, dtype: object\n",
      "Value Counts: \n",
      " occupation\n",
      "Prof-specialty       6172\n",
      "Craft-repair         6112\n",
      "Exec-managerial      6086\n",
      "Adm-clerical         5611\n",
      "Sales                5504\n",
      "Other-service        4923\n",
      "Machine-op-inspct    3022\n",
      "?                    2809\n",
      "Transport-moving     2355\n",
      "Handlers-cleaners    2072\n",
      "Farming-fishing      1490\n",
      "Tech-support         1446\n",
      "Protective-serv       983\n",
      "Priv-house-serv       242\n",
      "Armed-Forces           15\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** relationship **********\n",
      "count                                                  48842\n",
      "nunique                                                    6\n",
      "unique     [Own-child, Husband, Not-in-family, Unmarried,...\n",
      "Name: relationship, dtype: object\n",
      "Value Counts: \n",
      " relationship\n",
      "Husband           19716\n",
      "Not-in-family     12583\n",
      "Own-child          7581\n",
      "Unmarried          5125\n",
      "Wife               2331\n",
      "Other-relative     1506\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** race **********\n",
      "count                                                  48842\n",
      "nunique                                                    5\n",
      "unique     [Black, White, Asian-Pac-Islander, Other, Amer...\n",
      "Name: race, dtype: object\n",
      "Value Counts: \n",
      " race\n",
      "White                 41762\n",
      "Black                  4685\n",
      "Asian-Pac-Islander     1519\n",
      "Amer-Indian-Eskimo      470\n",
      "Other                   406\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** gender **********\n",
      "count               48842\n",
      "nunique                 2\n",
      "unique     [Male, Female]\n",
      "Name: gender, dtype: object\n",
      "Value Counts: \n",
      " gender\n",
      "Male      32650\n",
      "Female    16192\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** native_country **********\n",
      "count                                                  48842\n",
      "nunique                                                   42\n",
      "unique     [United-States, ?, Peru, Guatemala, Mexico, Do...\n",
      "Name: native_country, dtype: object\n",
      "Value Counts: \n",
      " native_country\n",
      "United-States                 43832\n",
      "Mexico                          951\n",
      "?                               857\n",
      "Philippines                     295\n",
      "Germany                         206\n",
      "Puerto-Rico                     184\n",
      "Canada                          182\n",
      "El-Salvador                     155\n",
      "India                           151\n",
      "Cuba                            138\n",
      "England                         127\n",
      "China                           122\n",
      "South                           115\n",
      "Jamaica                         106\n",
      "Italy                           105\n",
      "Dominican-Republic              103\n",
      "Japan                            92\n",
      "Guatemala                        88\n",
      "Poland                           87\n",
      "Vietnam                          86\n",
      "Columbia                         85\n",
      "Haiti                            75\n",
      "Portugal                         67\n",
      "Taiwan                           65\n",
      "Iran                             59\n",
      "Greece                           49\n",
      "Nicaragua                        49\n",
      "Peru                             46\n",
      "Ecuador                          45\n",
      "France                           38\n",
      "Ireland                          37\n",
      "Hong                             30\n",
      "Thailand                         30\n",
      "Cambodia                         28\n",
      "Trinadad&Tobago                  27\n",
      "Laos                             23\n",
      "Yugoslavia                       23\n",
      "Outlying-US(Guam-USVI-etc)       23\n",
      "Scotland                         21\n",
      "Honduras                         20\n",
      "Hungary                          19\n",
      "Holand-Netherlands                1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "********** income **********\n",
      "count              48842\n",
      "nunique                2\n",
      "unique     [<=50K, >50K]\n",
      "Name: income, dtype: object\n",
      "Value Counts: \n",
      " income\n",
      "<=50K    37155\n",
      ">50K     11687\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "discrete_univariate_analysis(discrete_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chi-Square Test for Goodness-of-fit\n",
    "\n",
    "Tests whether the observed frequencies of categorical data match the expected frequencies according to a specified distribution.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "- Observations should be discrete.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: The observed and expected frequencies are matching.\n",
    "- H1: The observed and expected frequencies are not matching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chi_square_good_fit(observed_frequencies, expected_frequencies, significance_level):\n",
    "    stat, p_value = stats.chisquare(observed_frequencies, expected_frequencies)\n",
    "    \n",
    "    print('stat=%.3f, p_value=%.3f' % (stat, p_value))\n",
    "    \n",
    "    if p_value < significance_level:\n",
    "        print('Reject null hypothesis (H0): The observed frequencies do not match the expected frequencies.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): The observed frequencies match the expected frequencies.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=4.800, p_value=0.028\n",
      "Reject null hypothesis (H0): The observed frequencies do not match the expected frequencies.\n"
     ]
    }
   ],
   "source": [
    "sample = df['gender'].sample(n=500)\n",
    "\n",
    "observed_freq = sample.value_counts().tolist()\n",
    "expected_freq = [300, 200]\n",
    "\n",
    "chi_square_good_fit(observed_freq, expected_freq, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Univariate Analysis - Numerical Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numerical_univariate_analysis(numerical_data):\n",
    "    for col_name in numerical_data:\n",
    "        print(\"*\"*10, col_name, \"*\"*10)\n",
    "        print(numerical_data[col_name].agg(['min', 'max', 'mean', 'median', 'std']))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** age **********\n",
      "min       17.000000\n",
      "max       90.000000\n",
      "mean      38.643585\n",
      "median    37.000000\n",
      "std       13.710510\n",
      "Name: age, dtype: float64\n",
      "\n",
      "********** fnlwgt **********\n",
      "min       1.228500e+04\n",
      "max       1.490400e+06\n",
      "mean      1.896641e+05\n",
      "median    1.781445e+05\n",
      "std       1.056040e+05\n",
      "Name: fnlwgt, dtype: float64\n",
      "\n",
      "********** educational_num **********\n",
      "min        1.000000\n",
      "max       16.000000\n",
      "mean      10.078089\n",
      "median    10.000000\n",
      "std        2.570973\n",
      "Name: educational_num, dtype: float64\n",
      "\n",
      "********** capital_gain **********\n",
      "min           0.000000\n",
      "max       99999.000000\n",
      "mean       1079.067626\n",
      "median        0.000000\n",
      "std        7452.019058\n",
      "Name: capital_gain, dtype: float64\n",
      "\n",
      "********** capital_loss **********\n",
      "min          0.000000\n",
      "max       4356.000000\n",
      "mean        87.502314\n",
      "median       0.000000\n",
      "std        403.004552\n",
      "Name: capital_loss, dtype: float64\n",
      "\n",
      "********** hours_per_week **********\n",
      "min        1.000000\n",
      "max       99.000000\n",
      "mean      40.422382\n",
      "median    40.000000\n",
      "std       12.391444\n",
      "Name: hours_per_week, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numerical_univariate_analysis(numerical_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 4, figsize=(10, 3), constrained_layout=True)\n",
    "fig.suptitle(\"Numerical Feature Visualization with Seaborn\")\n",
    "\n",
    "axs[0].set_title(\"Histogram\")\n",
    "sns.histplot(data=df, x='age', ax=axs[0])\n",
    "\n",
    "axs[1].set_title(\"KDE\")\n",
    "sns.kdeplot(data=df, x='age', ax=axs[1])\n",
    "\n",
    "axs[2].set_title(\"Box Plot\")\n",
    "sns.boxplot(data=df, x='age', ax=axs[2])\n",
    "\n",
    "stats.probplot(df['age'], dist=stats.norm, plot=axs[3])\n",
    "axs[3].grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look's like `age` is not normally distributed. But can we use some Statistical Test for same?\n",
    "\n",
    "### Shapiro Wilk Test\n",
    "The Shapiro-Wilk test is a statistical test that checks whether a dataset follows a normal distribution. It tests the null hypothesis that the data is normally distributed.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the sample has a Gaussian distribution.\n",
    "- H1: the sample does not have a Gaussian distribution.\n",
    "\n",
    "**Cons**\n",
    "- Cannot deal with more than 5000 data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shapiro_wilk(data, significance_level):\n",
    "    stat, p_value = stats.shapiro(data)\n",
    "    \n",
    "    print('stat=%.3f, p_value=%.3f' % (stat, p_value))\n",
    "    \n",
    "    if p_value < significance_level:\n",
    "        print('Reject null hypothesis (H0): Probably not Gaussian')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): Probably Gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.967, p_value=0.000\n",
      "Reject null hypothesis (H0): Probably not Gaussian\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\scipy\\stats\\_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "shapiro_wilk(df['age'], 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.965, p_value=0.000\n",
      "Reject null hypothesis (H0): Probably not Gaussian\n"
     ]
    }
   ],
   "source": [
    "data = df['age'].sample(n=4500)\n",
    "\n",
    "shapiro_wilk(data, 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kolmogorov-Smirnov Test\n",
    "\n",
    "The Kolmogorov-Smirnov (KS) test is used to check if a sample follows a specific distribution, including normal distribution. \n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the sample has a distribution.\n",
    "- H1: the sample does not have that distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kolmogorov_smirnov(data, significance_level):\n",
    "    # You can replace 'norm' with stats.norm\n",
    "    stat, p = stats.kstest(data, 'norm')\n",
    "    \n",
    "    print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "    \n",
    "    if p < significance_level:\n",
    "        print('Reject null hypothesis (H0): Probably not Gaussian')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): Probably Gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=1.000, p=0.000\n",
      "Reject null hypothesis (H0): Probably not Gaussian\n"
     ]
    }
   ],
   "source": [
    "kolmogorov_smirnov(df['age'], 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Sample t-test\n",
    "\n",
    "Tests whether the mean of a single sample is significantly different from a known or hypothesized population mean.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "- Observations are continuous measurements.\n",
    "- Data is normally distributed.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: $\\mu_{pop}=m_o$.\n",
    "- H1: $\\mu_{pop}\\ne m_o$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_sample_t_test(sample, assumed_pop_mean, significance_level):\n",
    "    stat, p_value = stats.ttest_1samp(sample, assumed_pop_mean)\n",
    "    \n",
    "    print('stat=%.3f, p_value=%.3f' % (stat, p_value))\n",
    "    \n",
    "    if p_value < significance_level:\n",
    "        print('Reject null hypothesis (H0): The sample mean is significantly different from the assumed population mean.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): The sample mean is not significantly different from the assumed population mean.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-18.878, p_value=0.000\n",
      "Reject null hypothesis (H0): The sample mean is significantly different from the assumed population mean.\n"
     ]
    }
   ],
   "source": [
    "sample = df['age'].sample(n=500)\n",
    "\n",
    "assumed_pop_mean = 50\n",
    "\n",
    "one_sample_t_test(sample, assumed_pop_mean, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Bivariate Analysis - Numerical vs Numerical**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='capital_loss'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='age', y='capital_loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pearson’s Correlation Coefficient\n",
    "\n",
    "Tests whether two samples have a linear relationship.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "- Observations in each sample are normally distributed.\n",
    "- Observations in each sample have the same variance.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the two samples are independent.\n",
    "- H1: there is a dependency between the samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pearson_test(data1, data2, significance_level):\n",
    "    stat, p_value = stats.pearsonr(data1, data2)\n",
    "    \n",
    "    print('stat=%.3f, p=%.3f' % (stat, p_value))\n",
    "    \n",
    "    if p_value < significance_level:\n",
    "        print('Reject null hypothesis (H0): Probably linear relationship exists')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): Probably linear relationship doesnot exists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.057, p=0.000\n",
      "Reject null hypothesis (H0): Probably linear relationship exists\n"
     ]
    }
   ],
   "source": [
    "pearson_test(df['age'], df['capital_loss'], significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Bivariate Analysis - Categorical vs Categorical**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Income vs Work Class')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, y='occupation', hue='income')\n",
    "\n",
    "plt.title('Income vs Work Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chi-Squared Test for Independence\n",
    "\n",
    "Tests whether two categorical variables are related or independent.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations used in the calculation of the contingency table are independent.\n",
    "- 25 or more examples in each cell of the contingency table.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the two samples are independent.\n",
    "- H1: there is a dependency between the samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chi_square(freq_table, significance_level):\n",
    "    stat, p_val, dof, expected = stats.chi2_contingency(freq_table)\n",
    "    \n",
    "    print('stat=%.3f, p=%.3f' % (stat, p_val))\n",
    "    \n",
    "    if p_val < significance_level:\n",
    "        print('Reject null hypothesis (H0): Probably dependent')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): Probably independent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>?</th>\n",
       "      <td>2544</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>4843</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed-Forces</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craft-repair</th>\n",
       "      <td>4729</td>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>3178</td>\n",
       "      <td>2908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Farming-fishing</th>\n",
       "      <td>1317</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handlers-cleaners</th>\n",
       "      <td>1934</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine-op-inspct</th>\n",
       "      <td>2650</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-service</th>\n",
       "      <td>4719</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Priv-house-serv</th>\n",
       "      <td>239</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>3388</td>\n",
       "      <td>2784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protective-serv</th>\n",
       "      <td>675</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>4029</td>\n",
       "      <td>1475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech-support</th>\n",
       "      <td>1026</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport-moving</th>\n",
       "      <td>1874</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income             <=50K  >50K\n",
       "occupation                    \n",
       "?                   2544   265\n",
       "Adm-clerical        4843   768\n",
       "Armed-Forces          10     5\n",
       "Craft-repair        4729  1383\n",
       "Exec-managerial     3178  2908\n",
       "Farming-fishing     1317   173\n",
       "Handlers-cleaners   1934   138\n",
       "Machine-op-inspct   2650   372\n",
       "Other-service       4719   204\n",
       "Priv-house-serv      239     3\n",
       "Prof-specialty      3388  2784\n",
       "Protective-serv      675   308\n",
       "Sales               4029  1475\n",
       "Tech-support        1026   420\n",
       "Transport-moving    1874   481"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_table = pd.crosstab(df['occupation'], df['income'])\n",
    "\n",
    "freq_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=5983.164, p=0.000\n",
      "Reject null hypothesis (H0): Probably dependent\n"
     ]
    }
   ],
   "source": [
    "chi_square(freq_table, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Bivariate Analysis - Numerical vs Categorical**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='age'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x='gender', y='age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two Sample t-test\n",
    "\n",
    "A two-sample t-test is applied when you want to compare the means of two independent groups to determine if they are significantly different from each other. It's used to test whether the observed differences in means between the groups are likely to occur due to random chance or if they are statistically significant.\n",
    "\n",
    "Tests whether the means of two independent samples are significantly different.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "- Observations in each sample are normally distributed.\n",
    "- Observations in each sample have the same variance.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the means of the samples are equal.\n",
    "- H1: the means of the samples are unequal.\n",
    "\n",
    "Remember, when applying a two-sample t-test, it's important to ensure that the assumptions of normality and equal variance are met. If these assumptions are violated, alternative tests like the Mann-Whitney U test or Welch's t-test (for unequal variances) may be more appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_sample_ttest(data1, data2, significance_level):\n",
    "    stat, p_val = stats.ttest_ind(data1, data2)\n",
    "    \n",
    "    print('stat=%.3f, p_val=%.3f' % (stat, p_val))\n",
    "    \n",
    "    if p_val < significance_level:\n",
    "        print('Reject null hypothesis (H0): There is a significant difference between the groups.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): There is no significant difference between the groups.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Male      32650\n",
       "Female    16192\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=1.206, p_val=0.229\n",
      "Fail to Reject null hypothesis (H0): Probably the same means.\n"
     ]
    }
   ],
   "source": [
    "male_age = df[df.gender=='Male']['age'].sample(n=100)\n",
    "female_age = df[df.gender=='Female']['age'].sample(n=100)\n",
    "\n",
    "two_sample_ttest(male_age, female_age, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marital Status vs Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marital_status\n",
       "Married-civ-spouse       22379\n",
       "Never-married            16117\n",
       "Divorced                  6633\n",
       "Separated                 1530\n",
       "Widowed                   1518\n",
       "Married-spouse-absent      628\n",
       "Married-AF-spouse           37\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='marital_status'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=df['marital_status'], x=df['age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Variance Test (ANOVA)\n",
    "\n",
    "ANOVA, or Analysis of Variance, is a statistical technique used to compare means between two or more groups. It's used to determine whether there are significant differences among the group means. ANOVA is particularly useful when you have more than two groups and you want to test if there are any differences in means across all the groups.\n",
    "\n",
    "Both the two-sample t-test and ANOVA (Analysis of Variance) are hypothesis tests used to compare means of different groups. Use a two-sample t-test when you have exactly two groups and want to compare their means. Use ANOVA when you have more than two groups and want to determine if there is a significant difference in means among any of the groups. \n",
    "\n",
    "**Types of ANOVA**\n",
    "1. **One-Way ANOVA:** Compares means across three or more groups when you have one independent variable.\n",
    "2. **Two-Way ANOVA:** Examines the effects of two independent variables on a dependent variable. It allows you to assess both main effects and interaction effects.\n",
    "\n",
    "\n",
    "Tests whether the means of more than two independent samples are significantly different.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "- Observations in each sample are normally distributed.\n",
    "- Observations in each sample have the same variance.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the means of the samples are equal.\n",
    "- H1: one or more of the means of the samples are unequal.\n",
    "\n",
    "#### Remember, ANOVA provides information about group differences, but it doesn't tell you which specific groups are different.\n",
    "\n",
    "**Ensure that your data meets the assumptions of ANOVA (e.g., normality, homogeneity of variances) for accurate interpretation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_way_anova(all_groups, significance_level):\n",
    "    stat, p_val = stats.f_oneway(*all_groups)\n",
    "    \n",
    "    print('stat=%.3f, p_val=%.3f' % (stat, p_val))\n",
    "    \n",
    "    if p_val < significance_level:\n",
    "        print('Reject null hypothesis (H0): There is a significant difference between the groups.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): There is no significant difference between the groups.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=756.434, p_val=0.000\n",
      "Reject null hypothesis (H0): There is a significant difference between the groups.\n"
     ]
    }
   ],
   "source": [
    "divorced_age = df[df['marital_status']=='Divorced']['age'].sample(n=300)\n",
    "never_married_age = df[df['marital_status']=='Never-married']['age'].sample(n=300)\n",
    "widowed_age = df[df['marital_status']=='Widowed']['age'].sample(n=300)\n",
    "\n",
    "all_groups = [divorced_age, never_married_age, widowed_age]\n",
    "one_way_anova(all_groups, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **This is not the end!**\n",
    "\n",
    "**Kahaani abhi baaki hai mere dost!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### **Non-parametric statistics**\n",
    "Non-parametric statistics do not make assumptions about the underlying distribution of the data. They are used when the data may not meet the assumptions of parametric tests.\n",
    "\n",
    "**Examples of Non-parametric Tests:**\n",
    "- Spearman's Rank Correlation `(Alternative for Pearson Test)`\n",
    "- Mann-Whitney U Test (Wilcoxon Rank-Sum Test) `(Alternative for 2-sample t-test)`\n",
    "- Kruskal-Wallis Test `(Alternative for One-Way ANOVA Test)`\n",
    "\n",
    "\n",
    "The Mann-Whitney U Test (also known as the Wilcoxon Rank-Sum Test) and the Kruskal-Wallis Test are both non-parametric statistical tests used to assess whether there are differences between independent groups. These tests are applicable when the assumptions of parametric tests (like t-tests or ANOVA) are not met, such as when the data is not normally distributed.\n",
    "\n",
    "### Spearman’s Rank Correlation\n",
    "Tests whether two samples have a monotonic relationship.\n",
    "\n",
    "**Assumptions**\n",
    "- Observations in each sample are independent and identically distributed (iid).\n",
    "- Observations in each sample can be ranked.\n",
    "\n",
    "**Interpretation**\n",
    "- H0: the two samples are independent.\n",
    "- H1: there is a dependency between the samples.\n",
    "\n",
    "\n",
    "### Mann-Whitney U Test:\n",
    "- **Purpose:** It is used to determine whether there is a difference in distribution between two independent groups. It is suitable for ordinal and continuous data.\n",
    "- **Null Hypothesis:** There is no difference between the distributions of the two groups.\n",
    "\n",
    "### Kruskal-Wallis Test:\n",
    "- **Purpose:** It is an extension of the Mann-Whitney U Test and is used when there are more than two independent groups. It assesses whether there are any statistically significant differences between the medians of the groups.\n",
    "- **Null Hypothesis:** There is no difference in distribution among the groups.\n",
    "\n",
    "In both cases, a low p-value (typically below a chosen significance level, e.g., 0.05) indicates that there is enough evidence to reject the null hypothesis and conclude that there are differences between the groups.\n",
    "\n",
    "These tests are robust to deviations from normality and are often used when analyzing data with unknown or non-normal distributions. Remember to check the assumptions and requirements of each test before applying them to your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spearman(data1, data2, significance_level):\n",
    "    stat, p_value = stats.spearmanr(data1, data2)\n",
    "    \n",
    "    print('stat=%.3f, p=%.3f' % (stat, p_value))\n",
    "    \n",
    "    if p_value < significance_level:\n",
    "        print('Reject null hypothesis (H0): Probably monotonic relationship exists.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): Probably monotonic relationship doesnot exists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mann_whitney_u(data1, data2, significance_level):\n",
    "    stat, p_val = stats.mannwhitneyu(data1, data2)\n",
    "    \n",
    "    print('stat=%.3f, p_val=%.3f' % (stat, p_val))\n",
    "    \n",
    "    if p_val < significance_level:\n",
    "        print('Reject null hypothesis (H0): There is a significant difference between the groups.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): There is no significant difference between the groups.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kruskal(all_groups, significance_level):\n",
    "    stat, p_val = stats.kruskal(*all_groups)\n",
    "    \n",
    "    print('stat=%.3f, p_val=%.3f' % (stat, p_val))\n",
    "    \n",
    "    if p_val < significance_level:\n",
    "        print('Reject null hypothesis (H0): There is a significant difference between the groups.')\n",
    "    else:\n",
    "        print('Fail to Reject null hypothesis (H0): There is no significant difference between the groups.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.058, p=0.000\n",
      "Reject null hypothesis (H0): Probably monotonic relationship exists.\n"
     ]
    }
   ],
   "source": [
    "spearman(df['age'], df['capital_loss'], significance_level=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=145560.500, p_val=0.000\n",
      "Reject null hypothesis (H0): Probably different means.\n"
     ]
    }
   ],
   "source": [
    "male_age = df[df.gender=='Male']['age'].sample(n=500)\n",
    "female_age = df[df.gender=='Female']['age'].sample(n=500)\n",
    "\n",
    "mann_whitney_u(male_age, female_age, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=502.326, p_val=0.000\n",
      "Reject null hypothesis (H0): There is a significant difference between the groups.\n"
     ]
    }
   ],
   "source": [
    "divorced_age = df[df['marital_status']=='Divorced']['age'].sample(n=300)\n",
    "never_married_age = df[df['marital_status']=='Never-married']['age'].sample(n=300)\n",
    "widowed_age = df[df['marital_status']=='Widowed']['age'].sample(n=300)\n",
    "\n",
    "all_groups = [divorced_age, never_married_age, widowed_age]\n",
    "\n",
    "kruskal(all_groups, significance_level=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Summary**\n",
    "\n",
    "| Where | Statistical Test | Type | Purpose | \n",
    "|:---|:---|:---|:---|\n",
    "| Univariate - Discrete Data | Chi-Square Test for Goodness-of-Fit | Parametric | Tests whether the observed frequencies of categorical data match the expected frequencies according to a specified distribution. | \n",
    "| Univariate - Continuous Data | Kolmogorov-Smirnov Test | Parametric | Tests whether a sample comes from a specific distribution (not limited to normal distribution). It can be used to compare any two continuous distributions. | \n",
    "| Univariate - Continuous Data | One-Sample t-test | Parametric | Tests whether the mean of a single sample is significantly different from a known or hypothesized population mean. | \n",
    "| Bivariate - Continuous vs Continuous Data | Pearson Test | Parametric | Tests whether two samples have a linear relationship. | \n",
    "| Bivariate - Categorical vs Categorical Data | Chi-Square Test of Independence | Parametric | Tests whether there is a significant association between two categorical variables in a contingency table. |\n",
    "| Bivariate - Categorical vs Numerical Data | Two-Sample t-test | Parametric | Compares the means of two related samples (e.g., before and after treatment) to determine if there is a significant difference. |\n",
    "| Bivariate - Categorical vs Numerical Data | One-Way ANOVA | Parametric | Compares the mean of three or more samples. It helps determine if there is a significant difference between the samples. |\n",
    "| Bivariate - Continuous vs Continuous Data | Spearman Test | Non-Parametric | Tests whether two samples have a monotonic relationship. | \n",
    "| Bivariate - Categorical vs Numerical Data | Mann-Whitney U Test | Non-Parametric | Compares the means of two related samples (e.g., before and after treatment) to determine if there is a significant difference. |\n",
    "| Bivariate - Categorical vs Numerical Data | Kruskal-Wallis Test | Non-Parametric | Compares the mean of three or more samples. It helps determine if there is a significant difference between the samples. |"
   ]
  }
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